IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v434y2022ics0096300322005343.html
   My bibliography  Save this article

Fault detection filtering for MNNs with dynamic quantization and improved protocol

Author

Listed:
  • Lin, An
  • Cheng, Jun
  • Cao, Jinde
  • Wang, Hailing
  • Alsaedi, Ahmed

Abstract

This paper concerns the fault detection filtering problem for discrete-time memristive neural networks with mixed time delays. An improved dynamic event-triggering protocol, whose multiple threshold functions are dynamically adjustable, is presented to decrease the utilization of limited resources and achieve desired performance. Two mutually independent Bernoulli variables are given to depicting the randomly occurring cyber-attacks. Meanwhile, a dynamic quantizer is established to account for restricted bandwidth efficiently. Based on the Lyapunov theory, sufficient conditions are derived to ensure the filtering error system is exponential mean square stable and desired performance. In the end, a numerical example is provided to verify the effectiveness of the proposed methodology.

Suggested Citation

  • Lin, An & Cheng, Jun & Cao, Jinde & Wang, Hailing & Alsaedi, Ahmed, 2022. "Fault detection filtering for MNNs with dynamic quantization and improved protocol," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    • Handle: RePEc:eee:apmaco:v:434:y:2022:i:c:s0096300322005343
    DOI: 10.1016/j.amc.2022.127460
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322005343
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127460?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Sha & Bao, Haibo, 2022. "Event-triggered synchronization of coupled memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    2. Xie, Lifei & Cheng, Jun & Wang, Hailing & Wang, Jiange & Hu, Mengjie & Zhou, Zhidong, 2022. "Memory-based event-triggered asynchronous control for semi-Markov switching systems," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuan, Manman & Luo, Xiong & Mao, Xue & Han, Zhen & Sun, Lei & Zhu, Peican, 2022. "Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    2. Shi, Sangli & Wang, Zhengxin & Song, Qiang & Xiao, Min & Jiang, Guo-Ping, 2022. "Leader-following quasi-bipartite synchronization of coupled heterogeneous harmonic oscillators via event-triggered control," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    3. Deng, Jie & Li, Hong-Li & Cao, Jinde & Hu, Cheng & Jiang, Haijun, 2023. "State estimation for discrete-time fractional-order neural networks with time-varying delays and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    4. Liu, Yiqun & Zhuang, Guangming & Zhao, Junsheng & Lu, Junwei & Wang, Zekun, 2023. "H∞.. admissibilization for time-varying delayed nonlinear singular impulsive jump systems based on memory state-feedback control," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    5. Guo, Runan & Xu, Shengyuan, 2023. "Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    6. Zhou, Chao & Wang, Chunhua & Yao, Wei & Lin, Hairong, 2022. "Observer-based synchronization of memristive neural networks under DoS attacks and actuator saturation and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    7. Chen, Wei & Yu, Yongguang & Hai, Xudong & Ren, Guojian, 2022. "Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    8. Zhao, Ningning & Qiao, Yuanhua, 2024. "Stability analysis of Clifford-valued memristor-based neural networks with impulsive disturbances and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    9. Shichao Jia & Cheng Hu & Haijun Jiang, 2024. "Fixed/Preassigned-Time Synchronization of Fuzzy Memristive Fully Quaternion-Valued Neural Networks Based on Event-Triggered Control," Mathematics, MDPI, vol. 12(9), pages 1-31, April.
    10. Tang, Tianfeng & Qin, Gang & Zhang, Bin & Cheng, Jun & Cao, Jinde, 2024. "Event-based asynchronous state estimation for Markov jump memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 473(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:434:y:2022:i:c:s0096300322005343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.